RuViewπ RuView: WiFi DensePose turns commodity WiFi signals into real-time human pose estimation, vital sign monitoring, and presence detection — all without a single pixel of video.

RuView

RuView transforms ordinary WiFi signals into real-time human sensing without cameras or wearables. Using Channel State Information (CSI) from low-cost ESP32 sensors, it detects people through walls, monitors vital signs like breathing and heart rate, and estimates body pose — all on edge hardware with zero cloud dependency.

Key Features

• WiFi-based pose estimation: Captures 17 COCO keypoints from radio signals using the WiFlow architecture, trained entirely without cameras
• Vital sign monitoring: Contactless detection of breathing rate (6-30 BPM) and heart rate (40-120 BPM) through walls and in darkness
• Presence and occupancy detection: Identifies people, counts room occupancy, and tracks entries/exits with 100% accuracy and 0.012ms latency
• Activity recognition: Classifies walking, sitting, gestures, falls, and sleep stages from temporal CSI patterns
• Real-time 3D point clouds: Fuses camera depth, WiFi CSI, and mmWave radar into unified spatial models
• Edge intelligence: Runs entirely on ESP32 mesh ($9/node) with spiking neural networks that adapt in under 30 seconds

Use Cases

• Smart home monitoring: Occupancy-aware automation, fall detection, and room-level activity recognition without cameras
• Healthcare and assisted living: Contactless vital sign monitoring, sleep quality assessment, and apnea screening for elderly care
• Security and access control: Through-wall presence detection, perimeter monitoring, and intrusion alerts across multiple rooms
• Research and RF sensing: WiFi-based human sensing platform for academic projects and novel signal processing applications

Who Is It For

RuView is designed for developers, researchers, and organizations building privacy-first sensing systems that require real-time human activity and vital sign monitoring without video capture. It's ideal for smart home automation, healthcare applications, security systems, and anyone exploring WiFi-based RF sensing on resource-constrained edge devices.